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Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

ABSTRACT: Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath [Park, Sunho et al. “An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.” Bioinformatics (2016): 1643-51. doi:10.1093/bioinformatics/btv692] to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner.

ORGANISM(S): Homo sapiens

PROVIDER: GSE183136 | GEO | 2022/01/01

REPOSITORIES: GEO

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Project description:Importance: Severe alcoholic hepatitis (AH) is a highly lethal disease with a 3-month mortality up to 50%. Corticosteroids are the current standard of care although nearly 40% of the patients do not respond and accurate pre-treatment predictors of survival are lacking. Objective: To develop a prognostic score based on molecular and clinical variables before initiation of corticosteroids. Design, Setting, and Participants: Gene expression profiling of fixed liver biopsy obtained for diagnosis of severe AH assessed in 3 independent cohorts (1 prospective cohort for prognostic gene signature and integrative score derivation, and 2 retrospective cohorts for validation). Samples were obtained from 4 European and 1 US medical centers between July 2006 and February 2015. Exposures: Biopsy proven severe AH patients treated with corticosteroids. Main Outcome Measures: Identification of a gene signature combined with a validated clinical index to develop an integrative prognostic score of survival without death or liver transplantation in a derivation cohort (n=71). Prognostic performance of the score was validated in an independent multi-center validation cohort 1 (n=48). Finally, the score was implemented in an FDA-approved clinical diagnostic platform (NanoString) and tested in another independent validation cohort 2 (n=20). Results: A prognostic score, integrating a 123-gene signature and the model for end-stage liver disease (gs-MELD score), was defined in the derivation cohort, in which poor- and good-prognosis patients were discriminated with a cut-off value of 2.66 at 3 months (event-free survival rates of 32% vs. 76%, respectively, p<.001) and 6 months (26% vs. 65%, respectively, p<.001). In the validation cohort 1, the score similarly discriminated poor- and good-prognosis patients at 3 months (43%, [95% CI, 26%-70%] vs. 96% [95% CI, 89%-100%], respectively, p<.001, c-index of 0.83 [95% CI, 0.66-0.99]) and 6 months (34% [95% CI, 18%-61%] vs 84% [95% CI, 72%-100%], respectively, p<.001, c-index of 0.80 [95% CI, 0.66-0.94]), and outperformed other existing clinical indices. The NanoString-based gs-MELD score remained predictive of 3- (p=.03) and 6-month (p=.009) even-free survival in the validation cohort 2. Conclusion and Relevance: The gs-MELD score, incorporating clinically applicable gene signature test and the MELD score, enables pre-treatment survival prediction in severe AH patients.

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Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

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